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| Filter results6 paper(s) found. |
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1. Agronomist-in-the-Loop Semantic 3D Reconstruction of Cotton Boll Morphology from UAV Imagery for Precision AgricultureStandard aerial photogrammetry is failing precision agriculture in one specific area: the detailed morphological assessment of complex, cluttered canopies. While creating a field-level map is trivial, recovering the geometry of a single cotton boll from a drone altitude of 30 meters is often mathematically intractable for standard Structure-from-Motion (SfM) solvers. These traditional pipelines depend on pixel-perfect consistency, which breaks down amidst... P. Sundaravadivel, H. Manjunatha, S. Borah, A. Anand, A. Price, H. Torbert, L. Tamil, T. Stroud |
2. A Multimodal Spectral-Robustness-LLM Pipeline for Non-Destructive Identification of Loropetalum chinense CultivarsProprietary cultivars of ornamental shrub Loropetalum chinense, particularly the visually and spectrally similar ‘Cerise Charm’, ‘Purple Daybreak’, and ‘Red Diamond’, derive their market value from the intensity and stability of anthocyanin pigmentation, a trait that degrades subtly under abiotic stress. Reliance on manual (visual) grading makes the industry vulnerable to these latent, pre-manifestation pigment losses, which are often detected only... P. Sundaravadivel, S. Borah, H. Manjunatha, S.P. Kumpatla, L. Tamil, P. Knight, T. Stroud |
3. Unified Detection and Weight Estimation of Small Fruits Using Multi-Task Vision Models in Precision AgricultureThis work presents a single computer vision model that can perform both object detection and image-level regression from the same input image. Many real applications, especially in agriculture, need information about individual objects as well as a global measurement for the entire image. When analyzing an image of small fruits such as different types of berries, grapes, currants, and muscadine grapes, it may be necessary to detect and classify... P. Sundaravadivel, T. Stroud, S. Borah, B.J. Sampson, P. Knight, S.P. Kumpatla, J.F. Ross |
4. Evaluation of Kriging Models and Variogram Structures for Daily Weather Interpolation Across Georgia, United StatesSpatial interpolation fills gaps between scattered weather stations to create continuous maps of variables like temperature. In Georgia, USA—a state with rolling hills in the north, coastal plains in the south, and the Appalachian foothills—this process is vital for accurate climate monitoring, irrigation scheduling, and crop-yield forecasting. Without reliable grids, downstream models suffer from bias or uncertainty. This study aimed to assess... |
5. Digital Agriculture in Dairy Farming: Connectivity Diagnosis and Barriers to Technology Adoption in an Agrotechnological DistrictThe integration of digital tools and communication systems has underpinned a profound transformation in global production chains, aiming to optimize farm operations through technological innovation. Within this context, the present study forms part of the Semear Digital project, coordinated by Embrapa, and is grounded in the premise that digital inclusion constitutes an indispensable strategy for the sustainability and competitiveness of contemporary dairy farming. The primary objective... M.D. Melo, C.M. Paiva, A.L. Oliveira, F.N. Maciel, G.C. Siqueira , M.R. Borges , G.S. Furtado, P.M. Leme |
6. AgGeoSampler: A Geospatial Open-Source Data Acquisition and Sampling Design Dashboard for Agricultural ApplicationsModern agricultural and environmental research increasingly depends on high-resolution geospatial data to support precise, site-specific decision-making. Advances in satellite remote sensing, unmanned aerial systems, and digital soil mapping have generated vast spatial datasets that capture fine-scale variability in vegetation health, soil properties, and terrain attributes. However, translating this wealth of information into effective field-sampling... A. Bhattarai, A. Jakhar, K. Poudel, A. Dhaliwal, L.M. Bastos |